Mixing states and secondary formation processes of organic nitrogen-containing single particles in Guangzhou, China.

Organic nitrogen (ON) compounds play a significant role in the light absorption of brown carbon and the formation of organic aerosols, however, the mixing state, secondary formation processes, and influencing factors of ON compounds are still unclear. This paper reports on the mixing state of ON-containing particles based on measurements obtained using a high-performance single particle aerosol mass spectrometer in January 2020 in Guangzhou. The ON-containing particles accounted for 21% of the total detected single particles, and the particle count and number fraction of the ON-containing particles were two times higher at night than during the day. The prominent increase in the content of ON-containing particles with the enhancement of NOx mainly occurred at night, and accompanied by high relative humidity and nitrate, which were associated with heterogeneous reactions between organics and gaseous NOx and/or NO3 radical. The synchronous decreases in ON-containing particles and the mass absorption coefficient of water-soluble extracts at 365 nm in the afternoon may be associated with photo-bleaching of the ON species in the particles. In addition, the positive matrix factorization analysis found five factors dominated the formation processes of ON particles, and the nitrate factor (33%) mainly contributed to the production of ON particles at night. The results of this study provide unique insights into the mixing states and secondary formation processes of the ON-containing particles.

Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification.

In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning.

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Influence of ozone pollution on the mixing state and formation of oxygenated organics containing single particles.

The formation and aging processes of oxygenated organic molecules (OOMs) are important for understanding the formation mechanisms of secondary organic aerosols (SOAs) in the field. In this study, we investigated the mixing states of OOM particles by identifying several oxygenated species along with the distributions of secondary organic carbon (SOC) during both clean and ozone (O3)-polluted periods in July and September of 2022 in Guangzhou, China. OOM-containing particles accounted for 57 % and 49 % of the total detected single particles in July and September, respectively. Most of the OOM particles were internally mixed with sulfate and nitrate, while elemental carbon and hydrocarbon species were absent. Despite the higher SOC/OC ratio in September (81 %) than it in July (72 %), comparative investigations of the mass spectra, diurnal patterns, and distributions of OOM particles revealed the same composition and aging states of OOMs in two O3 pollution periods. As the O3 concentration increased from the clean to the polluted periods, the ratio of SOC to OC increased along with the relative abundance of secondary OOM particles among total OOM particles. In contrast, the relative abundance of OC-type OOM particles gradually decreased, indicating the conversion of hydrocarbon species into OOMs as the SOC/OC ratio increased. Both the bulk analysis of SOC from filter measurement and the mixing states of OOM particles suggested that OOM production and degree of oxidation were higher in the O3-polluted periods than in the clean periods. These results elucidate the effects of O3 pollution on the OOM formation process and offer new perspectives for the joint investigation of SOA production based on filter sampling and single-particle measurements.

Characteristics of Volatile Organic Compounds Emitted from Airport Sources and Their Effects on Ozone Production.

In recent years, commercial air transport has increased considerably. However, the compositions and source profiles of volatile organic compounds (VOCs) emitted from aircraft are still not clear. In this study, the characteristics of VOCs (including oxygenated VOCs (OVOCs)) emitted from airport sources were measured at Shenzhen Bao'an International Airport. The results showed that the compositions and proportions of VOC species showed significant differences as the aircraft operating state changed. OVOCs were the dominant species and accounted for 63.17%, 58.44%, and 51.60% of the total VOC mass concentration during the taxiing, approach, and take-off stages. Propionaldehyde and acetone were the main OVOCs, and dichloromethane and 1,2-dichloroethane were the main halohydrocarbons. Propane had the highest proportion among all alkanes, while toluene and benzene were the predominant aromatic hydrocarbons. Compared with the source profiles of VOCs from construction machinery, the proportions of halogenated hydrocarbons and alkanes emitted from aircraft were significantly higher, as were those of propionaldehyde and acetone. OVOCs were still the dominant VOC species in aircraft emissions, and their calculated ozone formation potential (OFP) was much higher than that of other VOC species at all stages of aircraft operations. Acetone, propionaldehyde, formaldehyde, acetaldehyde, and ethylene were the greatest contributors to ozone production. This study comprehensively measured the distribution characteristics of VOCs, and its results will aid in the construction of a source profile inventory of VOCs emitted from aircraft sources in real atmospheric environments.